Artificial neural network based prediction of drill flank wear from motor current signals

In this work, a multilayer neural network with back propagation algorithm (BPNN) has been applied to predict the average flank wear of a high speed steel (HSS) drill bit for drilling on a mild steel work piece. Root mean square (RMS) value of the spindle motor current, drill diameter, spindle speed and feed-rate are inputs to the network, and drill wear is the output. Drilling experiments have been carried out over a wide range of cutting conditions and the effects of drill wear, cutting conditions (speed, drill diameter, feed-rate) on the spindle motor current have been investigated. The performance of the trained neural network has been tested for new cutting conditions, and found to be in very good agreement to the experimentally determined drill wear values. The accuracy of the prediction of drill wear using neural network is found to be better than that using regression model.